Business Knowledge Series course
This sequel to Introduction to Applied Econometrics focuses on intermediate and advanced topics in working with econometric models. This course enables analysts to better understand their economic/business landscape and to improve their ability to make sound forecasts. Through applications, participants gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills of participants in dealing with problems and issues related to business and economics, as well as to improve communication skills in reporting findings to decision makers.
Learn how to
- Detect and circumvent collinearity and ill-conditioning problems in econometric models.
- Detect and assess data outliers and leverage points.
- Detect structural change and test the stability of structural coefficients.
- Incorporate dynamic elements in econometric models, principally through the use of distributed lags.
- Use Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized ARCH (GARCH) models.
- Use qualitative choice models and censored response models.
- Use simultaneous-equation models.
- Use seemingly unrelated regression models.
- Use panel data in econometric applications (self-study).
Who should attend
Academicians, economists, forecasters, and government and business analysts
Before attending this course, you should:
- Have a basic knowledge of SAS software, including SAS procedures such as PROC REG, PROC AUTOREG, and PROC MODEL.
- Know the equivalent of the material covered in the Introduction to Applied Econometrics course, specifically data issues inherent with econometric models, the development and estimation of single-equation econometric models, hypothesis testing associated with these models, the construction and interpretation of dummy variables, and the detection and circumvention of serial correlation (autocorrelation) and heteroscedasticity.
- Have some knowledge pertaining to developing and evaluating ex-post and ex-ante forecasts.
This course addresses SAS/ETS software.